CN111158900B - Lightweight distributed parallel computing system and method - Google Patents
Lightweight distributed parallel computing system and method Download PDFInfo
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- CN111158900B CN111158900B CN201911246879.7A CN201911246879A CN111158900B CN 111158900 B CN111158900 B CN 111158900B CN 201911246879 A CN201911246879 A CN 201911246879A CN 111158900 B CN111158900 B CN 111158900B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
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Abstract
The invention discloses a lightweight distributed parallel computing system and a method, wherein the system comprises a heterogeneous computing node module, the heterogeneous computing node module comprises a plurality of host computing nodes which are communicated with each other, and a resource management module and a distributed computing program APP software module are respectively deployed on two computing nodes; an algorithm module is also included. The method comprises the following steps: the APP software module converts data to be processed into a distributed data set, the distributed data set and the algorithm module are divided respectively to obtain a plurality of sets of distributed sub-data sets and processed data operators, division results are deployed to a plurality of available computing nodes, each processed data operator calculates the corresponding distributed sub-data set, and all the computing results are integrated by the APP software module and then final distributed parallel computing results are output. The invention utilizes the heterogeneous computing nodes with dispersed space to carry out cooperative computing processing on the large-scale data, and can greatly improve the comprehensive efficiency of cooperative processing of the distributed data on the premise of limited information resources.
Description
Technical Field
The invention belongs to the field of lightweight computer data coprocessing, and particularly relates to a lightweight distributed parallel computing system and method.
Background
Task systems such as unmanned aerial vehicles, unmanned vehicle-mounted vehicles and the like belong to task-intensive equipment, and the group formed by the task systems and the task systems has the following characteristics in some complex cooperative work environments.
1) The naturally dispersed nature of information resources. Individuals have more or less computing, networking, storage, IO, etc. device resources, and in cooperative work, groups are often scattered in different locations of a space to achieve optimal work efficiency.
2) The performance of individual node information resources is relatively limited. Due to factors such as space volume, heat dissipation/endurance, performance, standardization, etc., an individual can typically only be equipped with a limited number of core shared resources (computing, networking, storage, IO, etc. resources).
3) And (4) meeting the intelligent cooperation requirement of group members. With the continuous increase of the number of the sensors and the continuous improvement of the precision, an advanced artificial intelligence technical mechanism is needed to filter, detect and judge a large amount of sensing information, so that efficient autonomous or semi-autonomous obstacle avoidance and decision assistance are realized, and the overall operation efficiency of a group is effectively improved.
4) The task application is diversified. In the group cooperative operation process, an information system needs to bear more and more various task applications with larger differences, such as data acquisition, information processing, real-time control and the like, and the task applications comprise various task types such as intensive calculation, time key, safety key and the like.
By identifying the characteristics of limited cruising ability, limited loading capacity, limited space volume and the like of the unmanned aerial vehicle and the unmanned vehicle, the unmanned aerial vehicle and the unmanned vehicle need to have the characteristics of high integration degree, complex software structure, strong external cooperation requirement, high autonomous intelligence requirement, seamless multiplexing of task packages (task loads and corresponding processing software) and the like. At present, unmanned on-board and unmanned on-board task systems have the problems of limited computing capability, limited expansion capability, low processing parallelism and the like. In order to improve the computing power, the expansion power and the processing parallelism of unmanned airborne and unmanned vehicular task systems, 2 common solutions are provided at present.
A solution is that a plurality of host nodes are equipped on an unmanned airborne and unmanned vehicle-mounted task system, hadoop or Spark distributed data processing framework software is installed and deployed, the distributed cooperative computing function of the plurality of host nodes is realized, and the problems of computing capability, expansion capability, processing parallelism and the like of the unmanned airborne and unmanned vehicle-mounted task system can be improved to a certain extent. However, the Hadoop and Spark have high requirements on infrastructure hardware, so that the problems of unmanned airborne vehicle-mounted mission system endurance limit and limited space volume are difficult to simultaneously meet.
The other solution is to provide online service for the unmanned airborne and unmanned vehicle-mounted task systems by utilizing the ultra-strong computing capability of the remote cloud computing center, and can ensure the problems of computing capability, expansion capability, processing parallelism and the like of the unmanned airborne and unmanned vehicle-mounted task systems to a great extent. However, stable and reliable network connection is required for obtaining the online service through the cloud computing center, and when the network is unstable or even unavailable due to factors such as distance, obstacles, special requirements and the like, the mode of excessively depending on the cloud computing center may cause that the unmanned airborne task system and the unmanned vehicle-mounted task system cannot work normally.
Disclosure of Invention
The invention aims to provide a lightweight distributed parallel computing system and method, and aims to solve the problems that an existing distributed data processing framework is high in requirements on basic hardware facilities and excessively depends on a cloud computing center.
The technical solution for realizing the purpose of the invention is as follows: a lightweight distributed parallel computing system, said system comprising a heterogeneous compute node module comprising N host compute nodes in communication with each other, wherein a resource management module is deployed on one of the host compute nodes A, and a distributed computing program APP software module is deployed on one of the host compute nodes B; the system also comprises an algorithm module corresponding to a distributed computing program APP software module for processing the distributed parallel computing task;
the resource management module is used for dynamically acquiring the state information of the heterogeneous computing node module to form a distributed parallel computing resource pool; the distributed computing program APP software module is used for processing the computing resources required by the distributed parallel computing task, notifying a plurality of available host computing nodes in the distributed parallel computing resource pool to start corresponding worker processes, and registering the worker processes to the distributed computing program APP software module; the usable host computer computing node starts the worker process, namely the usable host computer computing node starts a computing process corresponding to the APP software module of the distributed computing program;
the distributed computing program APP software module is used for converting data to be processed into a distributed data set according to state information of host computing nodes corresponding to all registered worker processes, dividing the distributed data set and the algorithm module respectively to obtain a plurality of groups of distributed sub-data sets and processed data operators, deploying the plurality of groups of distributed sub-data sets and processed data operators to the plurality of available host computing nodes, and computing the distributed sub-data sets on the host computing nodes where the distributed sub-data sets are located by the processed data operators; the system is also used for integrating the calculation results of all the data processing operators and outputting the final distributed parallel calculation result; the distributed data set refers to a formatted data set which can be processed by the APP software module of the distributed computing program.
Further, the N host computing nodes that communicate with each other communicate specifically through a network interconnection device.
Further, the calculation processing unit in the host calculation node comprises a CPU processing unit and/or a GPU processing unit and/or an FPGA processing unit.
Furthermore, the CPU processing unit and the GPU processing unit, and the CPU processing unit and the FPGA processing unit are connected in a high-speed PCIE mode.
Further, the state information of the heterogeneous compute node module includes the utilization rate of CPU processing units and/or GPU processing units and/or FPGA processing units on each host compute node, and the memory utilization rate and network speed of each host compute node.
Further, the resource management module dynamically acquires the state information of the heterogeneous computing node module through a network, wherein the network is a local area network formed by connecting IPMI ports of the host computing nodes together through an Ethernet switch.
Further, registering the worker process to the distributed computing program APP software module, specifically, sending a registration message to the distributed computing program APP software module by the worker process through a network.
Further, the distributed computing program APP software module is an application program designed and developed by a user according to self service requirements and a programming interface provided by distributed parallel computing.
Further, the distributed data set is a data set to be processed which satisfies the following three conditions:
(1) Invariable: data is read only and not writable;
(2) Partitioning: the data set can be divided into a plurality of sub data sets;
(3) Can be operated in parallel: the final result obtained by respectively performing synchronous calculation processing and integration on the distributed sub-data sets by the data processing operators on the plurality of host computer computing nodes is consistent with the result obtained by independently calculating the distributed data sets by one host computer computing node.
A lightweight distributed parallel computing method comprises the following steps:
and 7, judging whether the final calculation result meets a preset target or not, if so, finishing the distributed parallel calculation task, otherwise, returning to the step 2 to continue the calculation.
Compared with the prior art, the invention has the following remarkable advantages: the large-scale data are subjected to cooperative computing processing by utilizing the spatially dispersed heterogeneous host computing node, and the large-scale data have the functions or characteristics of supporting various processing units such as a CPU/GPU/FPGA and the like, supporting a distributed data set, supporting a C + + programming interface, supporting distributed deployment and the like, so that the large-scale data processing method has the advantages of openness, unified resource management, multiplied computing performance, easiness in development, strong expandability, simplicity in maintenance and the like, and can greatly improve the comprehensive efficiency of distributed data cooperative processing on the premise of limited information resources.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
FIG. 1 is a diagram illustrating a software module deployment for a lightweight distributed parallel computing system according to an embodiment of the present invention.
Fig. 2 is a block diagram of a computing processing unit of a host computing node of a lightweight distributed parallel computing system according to an embodiment of the present invention.
Fig. 3 is a block diagram of a computing processing unit of a host computing node of a lightweight distributed parallel computing system according to an embodiment of the present invention.
Fig. 4 is a block diagram of computing processing units of a host computing node of a lightweight distributed parallel computing system according to an embodiment of the present invention.
Fig. 5 is a block diagram of a lightweight distributed parallel computing system corresponding to fig. 2 according to an embodiment of the present invention.
Fig. 6 is a block diagram of the lightweight distributed parallel computing system corresponding to fig. 3 according to an embodiment of the present invention.
Fig. 7 is a block diagram of the lightweight distributed parallel computing system corresponding to fig. 4 according to an embodiment of the present invention.
Fig. 8 is a flowchart of a lightweight distributed parallel computing method in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, in conjunction with fig. 1, there is provided a lightweight distributed parallel computing system comprising a heterogeneous computing node module comprising N host computing nodes in communication with each other, wherein a resource management module 7 is deployed on one of the host computing nodes a, and a distributed computing program APP software module 10 is deployed on one of the host computing nodes B; the system also comprises an algorithm module corresponding to a distributed computing program APP software module 10 for processing the distributed parallel computing task;
the resource management module 7 is used for dynamically acquiring the state information of the heterogeneous computing node modules to form a distributed parallel computing resource pool; the distributed computing program APP software module 10 is used for notifying a plurality of available host computing nodes in a distributed parallel computing resource pool to start corresponding worker processes according to computing resources required for processing distributed parallel computing tasks and applied by the distributed computing program APP software module 10, and registering the worker processes to the distributed computing program APP software module 10; the available host computer computing node starts a worker process, namely the available host computer computing node starts a computing process corresponding to the distributed computing program APP software module 10;
the distributed computing program APP software module 10 is used for converting data to be processed into a distributed data set according to state information of host computing nodes corresponding to all registered worker processes, dividing the distributed data set and the algorithm module respectively to obtain a plurality of groups of distributed sub-data sets 9 and processed data operators 8, deploying the plurality of groups of distributed sub-data sets 9 and processed data operators 8 to a plurality of available host computing nodes, and performing computing processing on the distributed sub-data sets 9 on the host computing nodes where the distributed sub-data sets 9 are located by the processed data operators 8; the system is also used for integrating the results calculated by all the data processing operators 8 and outputting the final distributed parallel calculation result; the distributed data set refers to a formatted data set that can be processed by the distributed computing program APP software module 10.
Further, in one embodiment, in conjunction with fig. 1, the N host computing nodes in communication with each other communicate via the network interconnection device 6.
Further, in one embodiment, the computation processing unit in the host computation node includes a CPU processing unit and/or a GPU processing unit and/or an FPGA processing unit.
Specifically illustratively, in conjunction with fig. 2, the compute processing unit in the host compute node 1 includes only the CPU processing unit 2. The lightweight distributed parallel computing system 5 configured in this manner is shown in fig. 5.
Or specifically exemplarily, in conjunction with fig. 3, the computation processing unit in the host compute node 1 includes a CPU processing unit 2, a GPU processing unit 3. The lightweight distributed parallel computing system 5 thus configured is shown in fig. 6.
Or specifically exemplarily, in conjunction with fig. 4, the computation processing units in the host compute node 1 include a CPU processing unit 2, a GPU processing unit 3, and an FPGA processing unit 4. The lightweight distributed parallel computing system 5 thus configured is shown in fig. 7.
Further, in one embodiment, the CPU processing unit and the GPU processing unit, and the CPU processing unit and the FPGA processing unit are connected by a high-speed PCIE manner, and a corresponding driver and OPENCL operating environment are installed.
Further, in one embodiment, the state information of the heterogeneous compute node module includes usage rates of CPU processing units and/or GPU processing units and/or FPGA processing units on each host compute node, and monitoring state information such as memory usage rate and network speed of each host compute node.
Further, in one embodiment, the resource management module 7 dynamically collects the state information of the heterogeneous computing node modules through a network, where the network is a local area network formed by connecting IPMI ports of the host computing nodes together through an ethernet switch.
Further, in one embodiment, the registering the worker process to the distributed computing program APP software module 10 specifically means that the worker process sends a registration message to the distributed computing program APP software module 10 through a network.
Further, in one embodiment, the APP software module 10 of the distributed computing program is an application program designed and developed by a user according to a programming interface (e.g., C + + programming interface) provided by distributed parallel computing according to self service requirements.
Further, in one embodiment, the distributed data set is a data set to be processed that satisfies the following three conditions:
(1) Invariable: data is read only and can not be written;
(2) Partitioning: the data set can be divided into a plurality of sub data sets;
(3) Can be operated in parallel: the final result obtained by respectively performing synchronous calculation processing and integration on the distributed sub-data sets by the data processing operators on the plurality of host computer computing nodes is consistent with the result obtained by independently calculating the distributed data sets by one host computer computing node.
Based on any one of the above embodiments, in an embodiment, with reference to fig. 8, there is provided a lightweight distributed parallel computing method, including the following steps:
and 7, judging whether the final calculation result meets a preset target, if so, finishing the distributed parallel calculation task, otherwise, returning to the step 2 to continue the calculation.
In summary, the system of the present invention utilizes the spatially dispersed heterogeneous host computing node to perform cooperative computing processing on the large-scale data, and has functions or characteristics of supporting multiple types of processing units such as CPU/GPU/FPGA, supporting a distributed data set, supporting a C + + programming interface, supporting distributed deployment, and the like, thereby having the advantages of being open, unified resource management, multiplied in computing performance, easy to develop, strong in expandability, simple to maintain, and the like, and being capable of greatly improving the comprehensive efficiency of distributed data cooperative processing on the premise of limited information resources, and solving the problems that the existing distributed data processing framework has high requirements on basic hardware facilities and excessively depends on a cloud computing center.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A lightweight distributed parallel computing system, characterized in that the system comprises a heterogeneous computing node module, which comprises N host computing nodes in communication with each other, wherein a resource management module is deployed on one of the host computing nodes A, and a distributed computing program APP software module is deployed on one of the host computing nodes B; the system also comprises an algorithm module corresponding to a distributed computing program APP software module for processing the distributed parallel computing task;
the resource management module is used for dynamically acquiring the state information of the heterogeneous computing node module to form a distributed parallel computing resource pool; the distributed computing program APP software module is used for processing the computing resources required by the distributed parallel computing task, notifying a plurality of available host computing nodes in the distributed parallel computing resource pool to start corresponding worker processes, and registering the worker processes to the distributed computing program APP software module; the usable host computer computing node starts the worker process, namely the usable host computer computing node starts a computing process corresponding to the APP software module of the distributed computing program;
the distributed computing program APP software module is used for converting data to be processed into a distributed data set according to state information of host computing nodes corresponding to all registered worker processes, dividing the distributed data set and the algorithm module respectively to obtain a plurality of sets of distributed sub-data sets and processed data operators, deploying the plurality of sets of distributed sub-data sets and processed data operators to the plurality of available host computing nodes, and computing the distributed sub-data sets on the host computing nodes where the distributed sub-data sets are located by the processed data operators; the system is also used for integrating the calculation results of all the data processing operators and outputting the final distributed parallel calculation result; the distributed data set refers to a formatted data set which can be processed by the APP software module of the distributed computing program.
2. The lightweight distributed parallel computing system of claim 1, wherein said N host computing nodes in communication with each other communicate, in particular, through a network interconnection device.
3. The lightweight distributed parallel computing system of claim 1, wherein compute processing units in said host compute node comprise CPU processing units and or GPU processing units and or FPGA processing units.
4. The lightweight distributed parallel computing system of claim 3, wherein the CPU and GPU processing units, and the CPU and FPGA processing units are connected by a high-speed PCIE.
5. The lightweight distributed parallel computing system of claim 4, wherein the state information of the heterogeneous compute node modules includes utilization of CPU processing units and/or GPU processing units and/or FPGA processing units on each host compute node, and memory utilization, network speed of each host compute node.
6. The lightweight distributed parallel computing system according to claim 1 or 5, wherein the resource management module dynamically collects status information of the heterogeneous computing node modules via a network, wherein the network is a local area network formed by connecting IPMI ports of the host computing nodes together via an Ethernet switch.
7. The lightweight distributed parallel computing system of claim 1, wherein the registering of the worker process to the distributed computing program APP software module is performed by sending a registration message to the distributed computing program APP software module over a network.
8. The lightweight distributed parallel computing system of claim 1, wherein the distributed computing program APP software module is an application program designed and developed by a user according to business requirements of the user and according to a programming interface provided by the distributed parallel computing.
9. The lightweight distributed parallel computing system of claim 1, wherein the distributed data set is a set of pending data that satisfies the following three conditions:
(1) Invariable: data is read only and not writable;
(2) Partitioning: the data set can be divided into a plurality of sub data sets;
(3) The following operations can be performed in parallel: the final result obtained by respectively performing synchronous calculation processing and integration on the distributed sub-data sets by the data processing operators on the plurality of host computer computing nodes is consistent with the result obtained by independently calculating the distributed data sets by one host computer computing node.
10. The calculation method for the lightweight distributed parallel computing system according to any one of claims 1 to 9, comprising the steps of:
step 1, a resource management module dynamically acquires state information of a heterogeneous computing node module through a network to form a distributed parallel computing resource pool;
step 2, a distributed computing program APP software module applies for computing resources required for processing a distributed parallel computing task to a resource management module, and the resource management module informs a plurality of available host computing nodes to start corresponding worker processes according to available computing node resources in a distributed parallel computing resource pool;
step 3, registering the started worker process to a distributed computing program APP software module;
step 4, converting data to be processed into a distributed data set by a distributed computing program APP software module according to state information of host computing nodes corresponding to all registered worker processes, dividing the distributed data set and algorithm modules for processing distributed parallel computing tasks respectively to obtain a plurality of groups of distributed sub-data sets and processed data operators, deploying the plurality of groups of distributed sub-data sets and processed data operators to the plurality of available host computing nodes, and sending each group of processed data operators and the distributed sub-data sets to the worker process of each available host computing node;
step 5, each available host computer computing node worker process utilizes a data processing operator to perform computing processing on the corresponding distributed sub data set, and returns a computing result to the distributed computing program APP software module;
step 6, integrating all calculation results by a distributed calculation program APP software module to obtain a final calculation result;
and 7, judging whether the final calculation result meets a preset target or not, if so, finishing the distributed parallel calculation task, otherwise, returning to the step 2 to continue the calculation.
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